TF-MF: Improving Multiview Representation for Twitter User Geolocation Prediction

Abstract

Twitter user geolocation detection can inform and benefit a range of downstream geospatial tasks such as event and venue recommendation, local search, and crisis planning and response. In this paper, we take into account user shared tweets as well as their social network, and run extensive comparative studies to systematically analyze the impact of a variety of language-based, network-based, and hybrid methods in predicting user geolocation. In particular, we evaluate different text representation methods to construct text views that capture the linguistic signals available in tweets that are specific to and indicative of geographical locations. In addition, we investigate a range of network-based methods, such as embedding approaches and graph neural networks, in predicting user geolocation based on user interaction network. Our findings provide valuable insights into the design of effective and efficient geolocation identification engines. Finally, our best model, called TF-MF, substantially outperforms state-of-the-art approaches under minimal supervision.